Simulation of English teaching quality evaluation model based on gaussian process machine learning

2020 ◽  
pp. 1-11
Author(s):  
Huang Wenming
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Learning Algorithm ◽  
Teaching Quality ◽  
Distribution Characteristics ◽  
English Teaching ◽  
Traditional Evaluation

The efficiency of traditional English teaching quality evaluation is relatively low, and evaluation statistics are very troublesome. Traditional evaluation method makes teaching evaluation a difficult project, and traditional evaluation method takes a long time and has low efficiency, which seriously affects the school’s efficiency. In order to improve the quality of English teaching, based on machine learning technology, this study combines Gaussian process to improve the algorithm, use mixed Gaussian to explore the distribution characteristics of samples, and improve the classic relevance vector machine model. Moreover, this study proposes an active learning algorithm that combines sparse Bayesian learning and mixed Gaussian, strategically selects and labels samples, and constructs a classifier that combines the distribution characteristics of the samples. In addition, this study designed a control experiment to analyze the performance of the model proposed in this study. It can be seen from the comparison that this research model has a good performance in the evaluation of the English teaching quality of traditional models and online models. This shows that the algorithm proposed in this paper has certain advantages, and it can be applied to the practice of English intelligent teaching system.

scholarly journals Exploration on the Teaching Reform Measure for Machine Learning Course System of Artificial Intelligence Specialty

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yizhang Jiang ◽  
Bo Li
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Teaching Quality ◽  
Teaching Reform ◽  
Intelligent Technology ◽  
Quality Evaluation System

Due to the particularity of the artificial intelligence major and the machine learning courses learned, the traditional course teaching model is not suitable for artificial intelligence major machine learning courses. Based on this background, this article proposes a new system based on machine learning curriculum teaching reform. It mainly includes the reform of curriculum teaching mode, curriculum practice reform, and teaching process reform. In order to verify the effect of the proposed new model on the teaching quality of machine learning courses, this article also proposes an evaluation method based on intelligent technology. Firstly, the feasibility of evaluation based on intelligent technology is described. Secondly, it lists the application details of the existing teaching evaluation based on intelligent technology. Finally, a novel teaching quality evaluation system based on intelligent technology is proposed. The system collects student facial expression data and uses classification algorithms to make classification decisions on the data. The result of the decision can give feedback on the quality of classroom teaching. The comparison of experiments based on different intelligent technologies shows that the teaching quality evaluation system proposed in this article is feasible and effective.

Research on Evaluation and Analysis Model of Business English Teaching Based on Fuzzy Comprehensive Judgment

2014 ◽  
Vol 644-650 ◽  
pp. 5611-5614
Author(s):  
Chun Hua Mao
Keyword(s):  
Quality Evaluation ◽  
Subjective Experience ◽  
Evaluation Method ◽  
Expert Knowledge ◽  
Teaching Quality ◽  
Classroom Teaching ◽  
Traditional Teaching ◽  
Business English ◽  
English Teaching ◽  
Comprehensive Judgment

Compared with the traditional teaching quality evaluation method, Fuzzy Comprehensive Judgment Model. Business English classroom teaching evaluation is an important part of English teaching quality management in institutions of higher learning, and it is of vital significance for us to improve the quality of foreign language teaching. Compared with the traditional teaching quality evaluation method, fuzzy comprehensive judgment Model, based on expert knowledge and subjective experience, can use mathematical methods with rigorous logic to remove subjective elements as much as possible, and to reasonably determine the evaluation index weight; it may take advantage of scientific quantitative methods to characterize the qualitative issues in classroom teaching qualitative evaluation, so that the qualitative and quantitative analysis can get a better integration, which helps to overcome the subjective arbitrariness in English teaching quality evaluation, thus improving the reliability, accuracy and impartiality of the fuzzy comprehensive evaluation.

scholarly journals An Applicable Way of Teaching Quality Evaluation Based on MOOC Platform

2017 ◽  
Vol 12 (03) ◽  
pp. 57 ◽  
Author(s):  
Chen Zhuo ◽  
Xiaoming Dong
Keyword(s):  
Quality Evaluation ◽  
Evaluation Method ◽  
Control Experiment ◽  
Teaching Quality ◽  
Assessment Model ◽  
Fuzzy Evaluation ◽  
Teaching Management ◽  
Comprehensive Fuzzy Evaluation ◽  
Important Means

The MOOC-based education is an important means to improve the quality of education as the increasing development of internet; meanwhile, the assessment of teaching quality is an indispensable aspect in teaching management, and it has been more and more important as the scale of the students' expansion, In order to deal with the challenges of big data processing effectively in the field of education, we designed a teaching quality assessment model on the MOOC platform based on comprehensive fuzzy evaluation. To verify the effectiveness of our method, a control experiment was adopted to explore the significance of our evaluation method, the results show that it can help teacher to prepare their teaching contents and students to improve their learning efficiency.

Evaluation of English interpretation teaching quality based on GA optimized RBF neural network

2020 ◽  
pp. 1-8
Author(s):  
Lu Chen ◽  
He Being
Keyword(s):  
Neural Network ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Rbf Neural Network ◽  
Evaluation Model ◽  
Principal Component ◽  
Teaching Quality ◽  
Network Teaching ◽  
Time Performance

Aiming at the problem of low accuracy of the current English interpretation teaching quality evaluation, a teaching quality evaluation method based on a genetic algorithm (GA) optimized RBF neural network is proposed. First, the principal component analysis is used to select the teaching quality evaluation index, and then design The RBF neural network teaching evaluation model is used, and GA is used to optimize the initial weights of the RBF neural network. Experimental results show that this method can effectively evaluate the quality of English interpretation teaching, and has high accuracy and real-time performance.

scholarly journals Analyzing the Quality of Business English Teaching Using Multimedia Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yanyan Xin
Keyword(s):  
Data Mining ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Multimedia Data ◽  
Modern World ◽  
Business English ◽  
English Teaching ◽  
Using Data ◽  
Set Up

Data continually act as a substantial role in business and industry for its daily activities to smoothly functional. The data volume is growing with the passage of time and rising of information technology. Using data mining techniques for quality evaluation and business English teaching is essential in the modern world. These technologies are introduced in the classroom, especially in online classes during the COVID-19 pandemic. To analyze the quality of business English teaching, this paper uses multimedia and data mining technologies. Initially, the multimedia data are collected during classes, and the association rule recommendation algorithm using data mining is applied. Based on collaborative filtering algorithms in association rules, indicators for teaching quality evaluation in colleges and universities are set up. Next, the actual teaching data of a university is used. Taking business English as an example, the algorithm that has been built is tested. The application of the algorithm is tested, and the teaching process of College Business English is evaluated. Finally, the conclusion is drawn that data mining technology can describe the behavior of teaching well and evaluate it, and it has the potential of popularization.

Research on Classroom Teaching Quality Evaluation of Chinese as a Foreign Language Based on AHP Model

2013 ◽  
Vol 411-414 ◽  
pp. 2957-2960 ◽  
Author(s):  
Jing Liu
Keyword(s):  
Evaluation System ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Teaching Quality ◽  
Classroom Teaching ◽  
Ahp Model ◽  
Judgment Matrix ◽  
Quality Evaluation System ◽  
Chinese Teaching

How to evaluate the teaching quality of Chinese teacher objectively is an important subject of each university. In view of the shortage of classroom teaching quality evaluation, AHP model is introduced to evaluate the quality of TCFL, and an Chinese teaching quality evaluation system is established. Based on the evaluation content and standard of the system, combined with the the principle of AHP and expert investigation method, a judgment matrix is established, and the weight of each index to the total target is calculated. The comprehensive weight of each index and evaluation object score are multiplied, through a series of calculation, the teachers comprehensive scores can be obtained so as to evaluate the teaching quality. The results show that it is very scientific and objective to evaluate Chinese classroom teaching quality by using AHP model, it is a feasible evaluation method and has higher application value.

scholarly journals An English Teaching Ability Evaluation Algorithm Based on Frequency Effect

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lan Xu
Keyword(s):  
Evaluation System ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Frequency Effect ◽  
English Teaching ◽  
Teaching Objectives ◽  
Teaching Ability ◽  
Evaluation Algorithm ◽  
Ability Evaluation

Background. English is one of the courses offered in all colleges and universities. The quality of English teaching is directly related to the quality of talent training and the development of students themselves. “Teaching quality evaluation” specifically refers to the education evaluation with teaching as the evaluation object. It is the core and foundation of the whole education evaluation. Teaching quality evaluation is based on certain teaching objectives and teaching norms and standards, through the systematic detection and assessment of teaching and learning. Evaluate its teaching effect and the degree of realization of teaching objectives, and use scientific and feasible methods to make corresponding value judgments to improve the process of teaching. To improve the accuracy of English teaching ability evaluation, an English teaching ability evaluation algorithm based on frequency effect is proposed. Methods. The paper proposes an English teaching ability evaluation algorithm based on frequency effect. Firstly, it constructs the evaluation index system of English teaching ability, including expert evaluation system, student evaluation system, and teacher evaluation system. Then, the indexes affecting the evaluation of English teaching ability are quantified by fuzzy synthesis, and the evaluation indexes are refined. Finally, the basic principle of frequency effect is analyzed, combined with the convolutional neural network. Results. The convolutional neural network evaluation model is constructed, the teaching ability indicators are input into the model, the final evaluation results are output, and the design of the English teaching ability evaluation algorithm based on frequency effect is completed. Conclusions. The experimental results show that this method has high accuracy and efficiency.

scholarly journals Application of Optimized GA-BPNN Algorithm in English Teaching Quality Evaluation System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yaowu Zhu ◽  
Junnong Xu ◽  
Sihong Zhang
Keyword(s):  
Index System ◽  
Evaluation System ◽  
Quality Evaluation ◽  
Evaluation Method ◽  
Teaching Quality ◽  
Model Parameters ◽  
English Teaching ◽  
Comparison Results ◽  
Quality Evaluation System ◽  
Bpnn Model

The assessment of teaching quality is a very complex and fuzzy nonlinear process, which involves many factors and variables, so the establishment of the mathematical model is complicated, and the traditional evaluation method of teaching quality is no longer fully competent. In order to evaluate teaching quality effectively and accurately, an optimized GA-BPNN algorithm based on genetic algorithm (GA) and backpropagation neural network (BPNN) is proposed. Firstly, an index system of teaching quality evaluation is established, and a questionnaire is designed according to the index system to collect data. Then, an English teaching quality evaluation system is established by optimizing model parameters. The simulation shows that the average evaluation accuracy of the GA-BPNN algorithm is 98.56%, which is 13.23% and 5.85% higher than those of the BPNN model and the optimized BPNN model, respectively. The comparison results show that the GA-BPNN algorithm in teaching quality evaluation can make reasonable and scientific results.

Automatic verification method of progressive image model matching information based on machine learning

2021 ◽  
pp. 1-10
Author(s):  
Jie Yuan ◽  
Yuan Ji ◽  
Zhou Zhu ◽  
Liya Huang ◽  
Junfeng Qian ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Design Method ◽  
Large Error ◽  
Model Matching ◽  
Target Image ◽  
Image Model ◽  
Verification Method ◽  
Low Performance

In order to solve the problems of large error and low performance of traditional progressive image model matching information checking methods, an automatic progressive image model matching information checking method based on machine learning is proposed. The generation method of progressive image is analyzed, and the target image sample is obtained. On this basis, machine learning algorithm is used to segment progressive image samples. In each image segmentation part, crawler technology is used to automatically collect progressive image model matching information, and under the constraint of image model matching information checking standard, automatic checking of progressive image model matching information is realized from geometric structure, image content and other aspects. Experimental results show that the verification error of the design method is reduced by 0.687 Mb, and the quality of progressive image is improved.

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